Author: Nikolay Chehlarov date: 04.02.2022
paper: Image-to-Image Translation with Conditional Adversarial Networks
authors: Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros Berkeley AI Research (BAIR) Laboratory, UC Berkeley
Link to paper: https://arxiv.org/abs/1611.07004
Official implementation: https://github.com/phillipi/pix2pix.
The aim of the model is translate one to another image. Given input (picture) in one modality, output in different modality is generated. Training is done on pairs of pictures in two different modalities. Example pairs include satellite image - Google maps image, day - night, sketch - bag, semantic segment mask - real image and many more. One can provide a semantic segmented mask and obtain a realistic picture for example.
An on-line implementation can be used https://affinelayer.com/pixsrv/ to test the approach.
Traditionally, this tasks has been tackled with separate, special-purpose machinery, despite the fact that the setting is always the same: predict pixels from pixels. The goal in this paper is to develop a common framework for all these problems.

The pix2pix is based on conditional GAN architecture. The main building block of the pix2pix architecture are:
Unlike typical cGAN, the generator accepts combination of two losses - one from the discriminator (GAN loss, binary cross entropy) and one for the difference between the generated output to the input pair (the mask). The second loss is mean absolute error, and is referred as L1 loss. In the paper the combined loss is weighted sum, with L1 having weight of 100. Unlike an unconditional GAN, both the generator and discriminator observe the input edge map.
The objective of a conditional GAN can be expressed as $$ \mathscr{L}_{cGAN}(G, D)=E_{x, y}[logD(x, y))] + E_{x, y}[log(1 - D(x, G(x, z)))]$$ where G tries to minimize this objective against an adversarial D that tries to maximize it.

The conditional GAN the loss is learned, and can, in theory, penalize any possible structure that differs between output and target. Conditional GANs learn a mapping from observed image xr and random noise vector z, to y.
The implementation is originated from https://github.com/bnsreenu/python_for_microscopists/tree/master/251_satellite_image_to_maps_translation with minimal modifications. This source is based on the code by Jason Brownlee from his blogs on https://machinelearningmastery.com/.
The model is targeted to create maps from satellite images.
Generator:
Input - source image; Output - target image
The encoder-decoder architecture consists of:
encoder: C64-C128-C256-C512-C512-C512-C512-C512
decoder: CD512-CD512-CD512-C512-C256-C128-C64
Discriminator:
Input - pair of source and target image; output - probability between 0 and 1 that the inputs are actually a pair
C64-C128-C256-C512
After the last layer, a convolution is applied to map to a 1-dimensional output, followed by a Sigmoid function.
from matplotlib import pyplot as plt
from os import listdir
from datetime import datetime
import numpy as np
from numpy import asarray, load, vstack, savez_compressed, zeros, ones
from numpy.random import randint
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initializers import RandomNormal
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose, LeakyReLU, Activation, Concatenate, Dropout, BatchNormalization
from tensorflow.keras.utils import plot_model
from tensorflow.keras.preprocessing.image import img_to_array, load_img
#############################################################################
#Define generator, discriminator, gan and other helper functions
#We will use functional way of defining model and not sequential
#as we have multiple inputs; both images and corresponding labels.
########################################################################
def define_discriminator(image_shape):
# weight initialization
init = RandomNormal(stddev=0.02) #As described in the original paper
# source image input
in_src_image = Input(shape=image_shape) #Image we want to convert to another image
# target image input
in_target_image = Input(shape=image_shape) #Image we want to generate after training.
# concatenate images, channel-wise
merged = Concatenate()([in_src_image, in_target_image])
# C64: 4x4 kernel Stride 2x2
d = Conv2D(64, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(merged)
d = LeakyReLU(alpha=0.2)(d)
# C128: 4x4 kernel Stride 2x2
d = Conv2D(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
d = BatchNormalization()(d)
d = LeakyReLU(alpha=0.2)(d)
# C256: 4x4 kernel Stride 2x2
d = Conv2D(256, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
d = BatchNormalization()(d)
d = LeakyReLU(alpha=0.2)(d)
# C512: 4x4 kernel Stride 2x2
# This block is not in the original paper.
# d = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
# d = BatchNormalization()(d)
# d = LeakyReLU(alpha=0.2)(d)
# second last output layer : 4x4 kernel but Stride 1x1
d = Conv2D(512, (4,4), padding='same', kernel_initializer=init)(d)
d = BatchNormalization()(d)
d = LeakyReLU(alpha=0.2)(d)
# patch output
d = Conv2D(1, (4,4), padding='same', kernel_initializer=init)(d)
patch_out = Activation('sigmoid')(d)
# define model
model = Model([in_src_image, in_target_image], patch_out)
# compile model
#The model is trained with a batch size of one image and Adam opt.
#with a small learning rate and 0.5 beta1.
#The loss for the discriminator is weighted by 50% for each model update.
opt = Adam(learning_rate=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt, loss_weights=[0.5])
return model
# disc_model = define_discriminator((256,256,3))
# disc_model.summary()
def define_encoder_block(layer_in, n_filters, batchnorm=True):
# define an encoder block to be used in generator
init = RandomNormal(stddev=0.02)
g = Conv2D(n_filters, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(layer_in)
if batchnorm:
g = BatchNormalization()(g, training=True)
g = LeakyReLU(alpha=0.2)(g)
return g
def decoder_block(layer_in, skip_in, n_filters, dropout=True):
# define a decoder block to be used in generator
init = RandomNormal(stddev=0.02)
g = Conv2DTranspose(n_filters, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(layer_in)
g = BatchNormalization()(g, training=True)
if dropout:
g = Dropout(0.5)(g, training=True)
# merge with skip connection
g = Concatenate()([g, skip_in])
g = Activation('relu')(g)
return g
def define_generator(image_shape=(256,256,3)):
"""
Definint the generator, in this case U-net
Inputs should be in the range -1 to 1; generated outputs are also in this range
"""
init = RandomNormal(stddev=0.02)
in_image = Input(shape=image_shape)
# encoder model: C64-C128-C256-C512-C512-C512-C512-C512
e1 = define_encoder_block(in_image, 64, batchnorm=False)
e2 = define_encoder_block(e1, 128)
e3 = define_encoder_block(e2, 256)
e4 = define_encoder_block(e3, 512)
e5 = define_encoder_block(e4, 512)
e6 = define_encoder_block(e5, 512)
e7 = define_encoder_block(e6, 512)
# bottleneck, no batch norm
b = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(e7)
b = Activation('relu')(b)
# decoder model: CD512-CD512-CD512-C512-C256-C128-C64
d1 = decoder_block(b, e7, 512)
d2 = decoder_block(d1, e6, 512)
d3 = decoder_block(d2, e5, 512)
d4 = decoder_block(d3, e4, 512, dropout=False)
d5 = decoder_block(d4, e3, 256, dropout=False)
d6 = decoder_block(d5, e2, 128, dropout=False)
d7 = decoder_block(d6, e1, 64, dropout=False)
# output
g = Conv2DTranspose(image_shape[2], (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d7)
out_image = Activation('tanh')(g) #Generates images in the range -1 to 1.
# define model, without compile
model = Model(in_image, out_image)
return model
# gen_model = define_generator((256,256,3))
# gen_model.summary()
def define_gan(g_model, d_model, image_shape):
# define the combined generator and discriminator model, for updating the generator
# make weights in the discriminator not trainable
for layer in d_model.layers:
if not isinstance(layer, BatchNormalization):
layer.trainable = False #Descriminator layers set to untrainable in the combined GAN but
#standalone descriminator will be trainable.
# define the source image
in_src = Input(shape=image_shape)
# suppy the image as input to the generator
gen_out = g_model(in_src)
# supply the input image and generated image as inputs to the discriminator
dis_out = d_model([in_src, gen_out])
# src image as input, generated image and disc. output as outputs
model = Model(in_src, [dis_out, gen_out])
opt = Adam(learning_rate=0.0002, beta_1=0.5)
#Total loss is the weighted sum of adversarial loss (BCE) and L1 loss (MAE)
#Authors suggested weighting BCE vs L1 as 1:100.
model.compile(loss=['binary_crossentropy', 'mae'],
optimizer=opt,
loss_weights=[1, 100]
)
return model
def generate_real_samples(dataset, n_samples, patch_shape, pic_idx=None):
# select a batch of random samples, returns images and target
# unpack dataset
trainA, trainB = dataset # trainA is satellite image, trainB is coresponding maps image
if pic_idx == None:
# choose random instances
ix = randint(0, trainA.shape[0], n_samples)
else:
ix = np.array(pic_idx)
# retrieve selected images
X1, X2 = trainA[ix], trainB[ix]
# generate 'real' class labels (1)
y = ones((n_samples, patch_shape, patch_shape, 1))
return [X1, X2], y
def generate_fake_samples(g_model, samples, patch_shape):
# generate a batch of images, returns images and targets
# generate fake instance
X = g_model.predict(samples)
# create 'fake' class labels (0)
y = zeros((len(X), patch_shape, patch_shape, 1))
return X, y
def summarize_performance(step, g_model, data_set, n_samples=3, pic_idx=None):
"""
step : a strin gindicating the step of the trainning
generate samples and save as a plot and save the model
GAN models do not converge, we just want to find a good balance between
the generator and the discriminator. Therefore, it makes sense to periodically
save the generator model and check how good the generated image looks.
"""
# select a sample of input images
[X_realA, X_realB], _ = generate_real_samples(data_set, n_samples, 1, pic_idx=pic_idx)
# generate a batch of fake samples
X_fakeB, _ = generate_fake_samples(g_model, X_realA, 1)
# print("before")
# print(f"data_set max {data_set[0].max()}, min {data_set[0].min()}")
# print(f"shape {X_realA.shape}; min/max {X_realA.min()}/{X_realA.max()}")
# print(f"shape {X_realB.shape}; min/max {X_realB.min()}/{X_realB.max()}")
# print(f"shape {X_fakeB.shape}; min/max {X_fakeB.min()}/{X_fakeB.max()}")
# scale all pixels from [-1,1] to [0,1]
X_realA = (X_realA + 1) / 2.0
X_realB = (X_realB + 1) / 2.0
X_fakeB = (X_fakeB + 1) / 2.0
# plot real source images
fig = plt.figure(figsize=(10,10))
for i in range(n_samples):
plt.subplot(3, n_samples, 1 + i)
plt.axis('off')
plt.imshow(X_realA[i])
# plot generated target image
for i in range(n_samples):
plt.subplot(3, n_samples, 1 + n_samples + i)
plt.axis('off')
plt.imshow(X_fakeB[i])
# plot real target image
for i in range(n_samples):
plt.subplot(3, n_samples, 1 + n_samples*2 + i)
plt.axis('off')
plt.imshow(X_realB[i])
fig.suptitle(f'Model performance at {step} (src/gen/real)', fontsize=16)
# save plot to file
filename1 = 'plot_' + str(step) + '.png'
plt.savefig(filename1)
plt.show()
plt.close()
# save the generator model
filename2 = 'model_' + str(step) + '.h5'
g_model.save(filename2)
print('>Saved: %s and %s' % (filename1, filename2))
def train(d_model, g_model, gan_model, d_train, d_test, n_epochs=100, n_batch=1):
"""
train pix2pix models
10 epochs need about 1h on Quadro T1000
"""
# determine the output square shape of the discriminator
n_patch = d_model.output_shape[1]
# unpack dataset
trainA, trainB = d_train
# calculate the number of batches per training epoch
bat_per_epo = int(len(trainA) / n_batch)
# calculate the number of training iterations
n_steps = bat_per_epo * n_epochs
# manually enumerate epochs
for i in range(n_steps):
# select a batch of real samples
[X_realA, X_realB], y_real = generate_real_samples(d_train, n_batch, n_patch)
# generate a batch of fake samples
X_fakeB, y_fake = generate_fake_samples(g_model, X_realA, n_patch)
# update discriminator for real samples
d_loss1 = d_model.train_on_batch([X_realA, X_realB], y_real)
# update discriminator for generated samples
d_loss2 = d_model.train_on_batch([X_realA, X_fakeB], y_fake)
# update the generator
g_loss, _, _ = gan_model.train_on_batch(X_realA, [y_real, X_realB]) #gan loss, d_model loss, g_model loss
# summarize performance
print(f'step{i+1}: d_loss_real[{d_loss1:.3f}] d_loss_fake[{d_loss2:.3f}] gan_loss[{g_loss:.3f}]')
# summarize model performance
if (i+1) % (bat_per_epo * 1) == 0:
# every epoch
step = "epoch" + str((i+1) / bat_per_epo)
summarize_performance(step, g_model, d_test, n_samples=3, pic_idx=[0, 2, 9])
Data from: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/maps.tar.gz
Other datasets can be found here: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/
def load_images(path, size=(256,512)):
"""
Load all images from a directory into memory
"""
src_list, tar_list = list(), list()
# enumerate filenames in directory, assume all are images
for filename in listdir(path):
# load and resize the image
pixels = load_img(path + filename, target_size=size)
# convert to numpy array
pixels = img_to_array(pixels)
# split into satellite and map
sat_img, map_img = pixels[:, :256], pixels[:, 256:]
src_list.append(sat_img)
tar_list.append(map_img)
return [asarray(src_list), asarray(tar_list)]
def preprocess_data(data):
# load compressed arrays
# unpack arrays
X1, X2 = data[0], data[1]
# scale from [0,255] to [-1,1]
X1 = (X1 - 127.5) / 127.5
X2 = (X2 - 127.5) / 127.5
return [X1, X2]
# train
path = 'maps/train/'
dataset_train = preprocess_data(load_images(path))
print('Loaded train: ', dataset_train[0].shape, dataset_train[1].shape)
# test dataset path
path = 'maps/test/'
dataset_test = preprocess_data(load_images(path))
print('Loaded train: ', dataset_test[0].shape, dataset_test[1].shape)
Loaded train: (1096, 256, 256, 3) (1096, 256, 256, 3) Loaded train: (32, 256, 256, 3) (32, 256, 256, 3)
# define input shape based on the loaded dataset
image_shape = dataset_train[0].shape[1:]
# define the models
d_model = define_discriminator(image_shape) # model
g_model = define_generator(image_shape) # model_1
gan_model = define_gan(g_model, d_model, image_shape) # model_2
start1 = datetime.now()
train(d_model, g_model, gan_model, dataset_train, dataset_test, n_epochs=20, n_batch=1)
#Reports parameters for each batch (total 1096) for each epoch.
#For 10 epochs we should see 10960
stop1 = datetime.now()
execution_time = stop1-start1
print("Execution time is: ", execution_time)
step1: d_loss_real[0.470] d_loss_fake[0.750] gan_loss[78.880] step2: d_loss_real[0.336] d_loss_fake[0.632] gan_loss[70.351] step3: d_loss_real[0.350] d_loss_fake[0.468] gan_loss[71.716] step4: d_loss_real[0.339] d_loss_fake[0.430] gan_loss[67.774] step5: d_loss_real[0.343] d_loss_fake[0.415] gan_loss[63.780] step6: d_loss_real[0.329] d_loss_fake[0.387] gan_loss[54.318] step7: d_loss_real[0.312] d_loss_fake[0.385] gan_loss[62.359] step8: d_loss_real[0.355] d_loss_fake[0.370] gan_loss[60.752] step9: d_loss_real[0.398] d_loss_fake[0.384] gan_loss[41.801] step10: d_loss_real[0.298] d_loss_fake[0.354] gan_loss[51.232] step11: d_loss_real[0.425] d_loss_fake[0.909] gan_loss[40.961] step12: d_loss_real[0.482] d_loss_fake[0.676] gan_loss[42.712] step13: d_loss_real[0.326] d_loss_fake[0.424] gan_loss[47.103] step14: d_loss_real[0.253] d_loss_fake[0.335] gan_loss[44.522] step15: d_loss_real[0.203] d_loss_fake[0.311] gan_loss[42.421] step16: d_loss_real[0.253] d_loss_fake[0.479] gan_loss[26.772] step17: d_loss_real[0.323] d_loss_fake[0.216] gan_loss[36.950] step18: d_loss_real[0.407] d_loss_fake[0.258] gan_loss[25.258] step19: d_loss_real[0.355] d_loss_fake[0.363] gan_loss[26.215] step20: d_loss_real[0.132] d_loss_fake[0.535] gan_loss[25.699] step21: d_loss_real[0.578] d_loss_fake[0.167] gan_loss[22.383] step22: d_loss_real[0.446] d_loss_fake[0.246] gan_loss[29.883] step23: d_loss_real[0.224] d_loss_fake[0.313] gan_loss[26.516] step24: d_loss_real[0.270] d_loss_fake[0.315] gan_loss[27.072] step25: d_loss_real[0.272] d_loss_fake[0.252] gan_loss[23.319] step26: d_loss_real[0.210] d_loss_fake[0.286] gan_loss[21.936] step27: d_loss_real[0.623] d_loss_fake[0.363] gan_loss[17.606] step28: d_loss_real[0.155] d_loss_fake[0.324] gan_loss[19.935] step29: d_loss_real[0.406] d_loss_fake[0.248] gan_loss[17.896] step30: d_loss_real[0.219] d_loss_fake[0.312] gan_loss[17.558] step31: d_loss_real[0.227] d_loss_fake[0.278] gan_loss[18.569] step32: d_loss_real[0.263] d_loss_fake[0.361] gan_loss[13.865] step33: d_loss_real[0.789] d_loss_fake[0.379] gan_loss[23.743] step34: d_loss_real[0.380] d_loss_fake[0.268] gan_loss[17.692] step35: d_loss_real[0.255] d_loss_fake[0.467] gan_loss[11.812] step36: d_loss_real[0.384] d_loss_fake[0.249] gan_loss[13.465] step37: d_loss_real[0.421] d_loss_fake[0.277] gan_loss[11.771] step38: d_loss_real[0.375] d_loss_fake[0.501] gan_loss[12.011] step39: d_loss_real[0.356] d_loss_fake[0.317] gan_loss[13.777] step40: d_loss_real[0.669] d_loss_fake[0.249] gan_loss[17.766] step41: d_loss_real[0.271] d_loss_fake[0.484] gan_loss[10.726] step42: d_loss_real[0.392] d_loss_fake[0.313] gan_loss[11.201] step43: d_loss_real[0.313] d_loss_fake[0.470] gan_loss[21.876] step44: d_loss_real[0.524] d_loss_fake[0.244] gan_loss[9.634] step45: d_loss_real[0.320] d_loss_fake[0.370] gan_loss[10.819] step46: d_loss_real[0.349] d_loss_fake[0.355] gan_loss[9.676] step47: d_loss_real[0.573] d_loss_fake[0.393] gan_loss[21.850] step48: d_loss_real[0.413] d_loss_fake[0.381] gan_loss[9.491] step49: d_loss_real[0.583] d_loss_fake[0.267] gan_loss[19.409] step50: d_loss_real[0.435] d_loss_fake[0.423] gan_loss[12.414] step51: d_loss_real[0.317] d_loss_fake[0.417] gan_loss[10.078] step52: d_loss_real[0.397] d_loss_fake[0.352] gan_loss[14.124] step53: d_loss_real[0.405] d_loss_fake[0.355] gan_loss[8.262] step54: d_loss_real[0.374] d_loss_fake[0.311] gan_loss[13.664] step55: d_loss_real[0.365] d_loss_fake[0.284] gan_loss[15.393] step56: d_loss_real[0.304] d_loss_fake[0.323] gan_loss[18.366] step57: d_loss_real[0.415] d_loss_fake[0.430] gan_loss[10.405] step58: d_loss_real[0.465] d_loss_fake[0.234] gan_loss[26.328] step59: d_loss_real[0.377] d_loss_fake[0.419] gan_loss[14.110] step60: d_loss_real[0.421] d_loss_fake[0.387] gan_loss[10.137] step61: d_loss_real[0.367] d_loss_fake[0.245] gan_loss[18.709] step62: d_loss_real[0.461] d_loss_fake[0.355] gan_loss[14.823] step63: d_loss_real[0.442] d_loss_fake[0.248] gan_loss[25.680] step64: 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WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. >Saved: plot_epoch2.0.png and model_epoch2.0.h5 step2193: d_loss_real[0.166] d_loss_fake[0.246] gan_loss[12.453] step2194: d_loss_real[0.133] d_loss_fake[0.207] gan_loss[8.483] step2195: d_loss_real[0.034] d_loss_fake[0.206] gan_loss[12.797] step2196: d_loss_real[0.131] d_loss_fake[0.170] gan_loss[12.861] step2197: d_loss_real[0.006] d_loss_fake[0.168] gan_loss[18.121] step2198: d_loss_real[0.512] d_loss_fake[0.190] gan_loss[6.985] step2199: d_loss_real[0.071] d_loss_fake[0.179] gan_loss[8.717] step2200: d_loss_real[0.003] d_loss_fake[0.104] gan_loss[13.238] step2201: d_loss_real[0.004] d_loss_fake[0.071] gan_loss[18.537] step2202: d_loss_real[0.261] d_loss_fake[0.296] gan_loss[6.155] step2203: d_loss_real[0.137] d_loss_fake[0.340] gan_loss[6.124] step2204: d_loss_real[0.581] d_loss_fake[0.158] gan_loss[9.372] 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d_loss_fake[0.281] gan_loss[4.994] step21905: d_loss_real[0.111] d_loss_fake[0.314] gan_loss[6.991] step21906: d_loss_real[0.125] d_loss_fake[0.244] gan_loss[6.329] step21907: d_loss_real[0.284] d_loss_fake[0.268] gan_loss[6.029] step21908: d_loss_real[0.230] d_loss_fake[0.207] gan_loss[5.439] step21909: d_loss_real[0.177] d_loss_fake[0.320] gan_loss[5.960] step21910: d_loss_real[0.160] d_loss_fake[0.246] gan_loss[5.460] step21911: d_loss_real[0.220] d_loss_fake[0.373] gan_loss[5.519] step21912: d_loss_real[0.299] d_loss_fake[0.231] gan_loss[5.494] step21913: d_loss_real[0.159] d_loss_fake[0.187] gan_loss[4.995] step21914: d_loss_real[0.188] d_loss_fake[0.258] gan_loss[4.191] step21915: d_loss_real[0.127] d_loss_fake[0.390] gan_loss[6.595] step21916: d_loss_real[0.304] d_loss_fake[0.181] gan_loss[6.640] step21917: d_loss_real[0.290] d_loss_fake[0.351] gan_loss[5.954] step21918: d_loss_real[0.301] d_loss_fake[0.319] gan_loss[6.662] step21919: d_loss_real[0.227] d_loss_fake[0.229] gan_loss[6.413] step21920: d_loss_real[0.309] d_loss_fake[0.466] gan_loss[5.743] before data_set max 1.0, min -1.0 shape (3, 256, 256, 3); min/max -1.0/1.0 shape (3, 256, 256, 3); min/max -1.0/1.0 shape (3, 256, 256, 3); min/max -0.8557028770446777/1.0
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model. >Saved: plot_epoch20.0.png and model_epoch20.0.h5 Execution time is: 1:58:04.921367
The trainned model after 20 epochs can be downloaded from here: https://drive.google.com/file/d/1LvZ5izBhOUAh1qDHDHUK-ZlV703kwzmi/view?usp=sharing
model = g_model
# model = load_model('model_epoch20.0.h5') # the model after 20 epochs
def plot_images(src_img, gen_img, tar_img):
"""
plot source, generated and target images
"""
images = vstack((src_img, gen_img, tar_img))
images = (images + 1) / 2.0 # scale from [-1,1] to [0,1]
titles = ['Source', 'Generated', 'Expected']
# plot images row by row
for i in range(len(images)):
plt.subplot(1, 3, 1 + i)
plt.axis('off')
plt.imshow(images[i])
plt.title(titles[i])
plt.show()
[X1, X2] = dataset_test
ix = randint(0, len(X1), 1)
src_image, tar_image = X1[ix], X2[ix]
# generate image from source
gen_image = model.predict(src_image)
plt.figure(figsize=(14,8))
plot_images(src_image, gen_image, tar_image)
Looking into the evolution of the model with training, the improvement is slow after the first 10 epochs. The training is short (only 20 epochs), but already delivered promising results. Possible and practical improvement might be to use several satellite images, taken during different season/camera angle/light direction.
pix2pix model is used to translate image from one domain to another. It was demonstrated how to train and convert satellite images to the corresponding maps images.